Acoustic analysis
# print link to github repo if any
if (file.exists("./.git/config")){
config <- readLines("./.git/config")
url <- grep("url", config, value = TRUE)
url <- gsub("\\turl = |.git$", "", url)
cat("\nSource code and data found at [", url, "](", url, ")", sep = "")
}# options to customize chunk outputs
knitr::opts_chunk$set(
tidy.opts = list(width.cutoff = 65),
tidy = TRUE,
message = FALSE
)
# funnction to print tables in html format
print_kable <- function(x) {
kb <- kable(x, row.names = TRUE, digits = 4, "html")
kb <- kable_styling(kb,
bootstrap_options = c("striped", "hover", "condensed", "responsive"))
scroll_box(kb, width = "100%")
}
You can have the sections listed here, for instance:
flowchart A[Read data] --> B(Create extended<br>selection tables) B --> C(Measure acoustic<br>features) C --> D(Explore acoustic spaces) style A fill:#44015466 style B fill:#3E4A894D style C fill:#26828E4D style D fill:#6DCD594D
# knitr is require for creating html/pdf/word reports formatR is
# used for soft-wrapping code
# install/ load packages
sketchy::load_packages(packages = c("knitr", "formatR", "warbleR",
"ggplot2", "maRce10/PhenotypeSpace"))# read laughs
est_laughs <- warbleR::selection_table(path = "./data/raw/audio_files/laughs/",
whole.recs = TRUE, extended = TRUE)
est_laughs <- resample_est(est_laughs, samp.rate = 22.05)
dat_laughs <- read.csv("./data/raw/audio_files/laughs/TBP_Laugh1stNote-CallList_20Jul25.csv")
dat_laughs$CallFile <- paste0(gsub("\\.aif", "", dat_laughs$CallFile),
"_1")
merged_laughs <- merge(est_laughs, dat_laughs, by.x = "sound.files",
by.y = "CallFile", all.x = TRUE)
merged_laughs <- fix_extended_selection_table(merged_laughs, est_laughs)
merged_laughs$type <- "laughs"
# read barks
est_barks <- warbleR::selection_table(path = "./data/raw/audio_files/barks",
whole.recs = TRUE, extended = TRUE)
est_barks <- resample_est(est_barks, samp.rate = 22.05)
dat_barks <- read.csv("./data/raw/audio_files/barks/TBP_Bark-CallList_20Jul25.csv")
dat_barks <- dat_barks[(dat_barks$Call.File) != "", ]
names(dat_barks) <- names(dat_laughs)
dat_barks$CallFile <- paste0(gsub("\\.aif", ".wav", dat_barks$CallFile),
"_1")
merged_barks <- merge(est_barks, dat_barks, by.x = "sound.files",
by.y = "CallFile", all.x = TRUE)
merged_barks <- fix_extended_selection_table(merged_barks, est_barks)
merged_barks$type <- "barks"
tbp_calls <- rbind(merged_laughs, merged_barks)
saveRDS(tbp_calls, file = "./data/processed/extended_selection_table_tpb_calls.rds")tbp_calls <- readRDS("./data/processed/extended_selection_table_tpb_calls.rds")
# cross correlation
tbp_xc <- warbleR::cross_correlation(X = tbp_calls, ovlp = 70, bp = c(0.1,
8), parallel = 20)
# save results
saveRDS(tbp_xc, file = "./data/processed/cross_correlation_tpb_calls.rds")
# mfccs
tbp_mfcc <- warbleR::mfcc_stats(X = tbp_calls, ovlp = 70, bp = c(0.1,
8), parallel = 20)
# save results
saveRDS(tbp_mfcc, file = "./data/processed/mfcc_tpb_calls.rds")
# mfccs
tbp_spft <- warbleR::spectro_analysis(X = tbp_calls, ovlp = 70, bp = c(0.1,
8), parallel = 20)
# save results
saveRDS(tbp_spft, file = "./data/processed/spectral_features_tpb_calls.rds")Using cross-correlation:
tbp_xc <- readRDS("./data/processed/cross_correlation_tpb_calls.rds")
tbp_calls <- readRDS("./data/processed/extended_selection_table_tpb_calls.rds")
tbp_mds <- cmdscale(tbp_xc, k = 2)
tbp_calls$MDS1 <- tbp_mds[, 1]
tbp_calls$MDS2 <- tbp_mds[, 2]
ss_type <- space_similarity(type ~ MDS1 + MDS2, data = as.data.frame(tbp_calls),
method = "density.overlap")
# graph bidimensional space with gpplot coloring by type
ggplot(tbp_calls, aes(x = MDS1, y = MDS2, color = type)) + geom_point() +
scale_colour_viridis_d(option = "G", begin = 0.2, end = 0.8, name = "Call type") +
theme_classic(base_size = 20) + labs(title = paste0("Mean density overlap:",
round(ss_type$mean.overlap, 2)), x = "MDS1", y = "MDS2")
ss_type_barks <- space_similarity(Site ~ MDS1 + MDS2, data = as.data.frame(tbp_calls[tbp_calls$type ==
"barks", ]), method = "density.overlap")
ss_type_laughs <- space_similarity(Site ~ MDS1 + MDS2, data = as.data.frame(tbp_calls[tbp_calls$type ==
"laughs", ]), method = "density.overlap")
mean_ovlp <- mean(c(ss_type_barks$mean.overlap, ss_type_laughs$mean.overlap))
ggplot(tbp_calls, aes(x = MDS1, y = MDS2, color = Site)) + geom_point() +
scale_colour_viridis_d(option = "G", begin = 0.2, end = 0.8, name = "Site") +
theme_classic(base_size = 20) + facet_grid(~type, scales = "free") +
labs(title = paste0("Mean density overlap:", round(mean_ovlp,
2)), x = "MDS1", y = "MDS2")
Using MFCCs:
tbp_mfcc <- readRDS("./data/processed/mfcc_tpb_calls.rds")
# pca
pca <- prcomp(tbp_mfcc[, -c(1:2)], center = TRUE, scale. = TRUE)
tbp_calls$PC1.mfcc <- pca$x[, 1]
tbp_calls$PC2.mfcc <- pca$x[, 2]
ss_type <- space_similarity(type ~ PC1.mfcc + PC2.mfcc, data = as.data.frame(tbp_calls),
method = "density.overlap")
# graph bidimensional space with gpplot coloring by type
ggplot(tbp_calls, aes(x = PC1.mfcc, y = PC2.mfcc, color = type)) +
geom_point() + scale_colour_viridis_d(option = "G", begin = 0.2,
end = 0.8, name = "Call type") + theme_classic(base_size = 20) +
labs(title = paste0("Mean density overlap:", round(ss_type$mean.overlap,
2)), x = "PC1", y = "PC2")
ss_type_barks <- space_similarity(Site ~ PC1.mfcc + PC2.mfcc, data = as.data.frame(tbp_calls[tbp_calls$type ==
"barks", ]), method = "density.overlap")
ss_type_laughs <- space_similarity(Site ~ PC1.mfcc + PC2.mfcc, data = as.data.frame(tbp_calls[tbp_calls$type ==
"laughs", ]), method = "density.overlap")
mean_ovlp <- mean(c(ss_type_barks$mean.overlap, ss_type_laughs$mean.overlap))
ggplot(tbp_calls, aes(x = PC1.mfcc, y = PC2.mfcc, color = Site)) +
geom_point() + scale_colour_viridis_d(option = "G", begin = 0.2,
end = 0.8, name = "Site") + theme_classic(base_size = 20) + facet_grid(~type,
scales = "free") + labs(title = paste0("Mean density overlap:",
round(mean_ovlp, 2)), x = "PC1", y = "PC2")
Using spectral features:
tbp_spft <- readRDS("./data/processed/spectral_features_tpb_calls.rds")
# pca
pca <- prcomp(tbp_spft[, -c(1:2)], center = TRUE, scale. = TRUE)
tbp_calls$PC1.spft <- pca$x[, 1]
tbp_calls$PC2.spft <- pca$x[, 2]
ss_type <- space_similarity(type ~ PC1.spft + PC2.spft, data = as.data.frame(tbp_calls),
method = "density.overlap")
# graph bidimensional space with gpplot coloring by type
ggplot(tbp_calls, aes(x = PC1.spft, y = PC2.spft, color = type)) +
geom_point() + scale_colour_viridis_d(option = "G", begin = 0.2,
end = 0.8, name = "Call type") + theme_classic(base_size = 20) +
labs(title = paste0("Mean density overlap:", round(ss_type$mean.overlap,
2)), x = "PC1", y = "PC2")
ss_type_barks <- space_similarity(Site ~ PC1.spft + PC2.spft, data = as.data.frame(tbp_calls[tbp_calls$type ==
"barks", ]), method = "density.overlap")
ss_type_laughs <- space_similarity(Site ~ PC1.spft + PC2.spft, data = as.data.frame(tbp_calls[tbp_calls$type ==
"laughs", ]), method = "density.overlap")
mean_ovlp <- mean(c(ss_type_barks$mean.overlap, ss_type_laughs$mean.overlap))
ggplot(tbp_calls, aes(x = PC1.spft, y = PC2.spft, color = Site)) +
geom_point() + scale_colour_viridis_d(option = "G", begin = 0.2,
end = 0.8, name = "Site") + theme_classic(base_size = 20) + facet_grid(~type,
scales = "free") + labs(title = paste0("Mean density overlap:",
round(mean_ovlp, 2)), x = "PC1", y = "PC2")
─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.5.0 (2025-04-11)
os Ubuntu 22.04.5 LTS
system x86_64, linux-gnu
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz America/Costa_Rica
date 2025-07-22
pandoc 3.2 @ /usr/lib/rstudio/resources/app/bin/quarto/bin/tools/x86_64/ (via rmarkdown)
quarto 1.7.31 @ /usr/local/bin/quarto
─ Packages ───────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
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* ── Packages attached to the search path.
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